R Notebook

library(tidyverse)

-- Attaching packages --------------------------------------- tidyverse 1.2.1 --
v tibble  1.4.2     v purrr   0.2.4
v tidyr   0.8.1     v dplyr   0.7.5
v readr   1.1.1     v stringr 1.3.1
v tibble  1.4.2     v forcats 0.3.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(readxl)
library(lubridate)

Attaching package: <U+393C><U+3E31>lubridate<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    date

aid <- read_excel("Aid_received.xlsx", sheet = "Data")
mapping <- read_excel("CountryMapping.xlsx", sheet = "Countries_etc")
aid_gathered <- aid %>%
  gather(key = 'year', value = 'aid', -`ODA received, total (constant 2010 US$)`)
country_aid <- inner_join(aid_gathered, 
                          mapping, by = c('ODA received, total (constant 2010 US$)' = 'name'))

library(janitor)
country_aid_clean <- clean_names(country_aid)
  
aid_all <- country_aid_clean %>%
  rename(country = oda_received_total_constant_2010_us) 

aid_all$year <- str_c(aid_all$year, '01','01', sep = "-")
aid_all$year <- ymd(aid_all$year)

glimpse(aid_all)

Observations: 9,996
Variables: 13
$ country                      <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "Antigua and Barbuda", "Argentina", "Armeni...
$ year                         <date> 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960...
$ aid                          <dbl> 108800000, NA, 3405040000, NA, -390000, NA, 91670000, NA, NA, NA, NA, -390000, 750000, NA, NA, NA, NA...
$ geo                          <chr> "afg", "alb", "dza", "and", "ago", "atg", "arg", "arm", "aus", "aut", "aze", "bhs", "bhr", "bgd", "br...
$ gapminders_4_world_regions   <chr> "asia", "europe", "africa", "europe", "africa", "americas", "americas", "europe", "asia", "europe", "...
$ gapminders_8_subregions      <chr> "asia_west", "europe_east", "africa_north", "europe_west", "africa_sub_saharan", "america_north", "am...
$ gapminders_6_world_regions   <chr> "south_asia", "europe_central_asia", "middle_east_north_africa", "europe_central_asia", "sub_saharan_...
$ members_of_g77_or_oecd       <chr> "g77", "others", "g77", "others", "g77", "g77", "g77", "others", "oecd", "oecd", "others", "g77", "g7...
$ latitude                     <dbl> 33.00000, 41.00000, 28.00000, 42.50779, -12.50000, 17.05000, -34.00000, 40.25000, -25.00000, 47.33333...
$ longitude                    <dbl> 66.00000, 20.00000, 3.00000, 1.52109, 18.50000, -61.80000, -64.00000, 45.00000, 135.00000, 13.33333, ...
$ un_member_sinces             <dttm> 1946-11-19, 1955-12-14, 1962-10-08, 1993-07-28, 1976-12-01, 1981-11-11, 1945-10-24, 1992-03-02, 1945...
$ world_bank_region            <chr> "South Asia", "Europe & Central Asia", "Middle East & North Africa", "Europe & Central Asia", "Sub-Sa...
$ world_bank_income_group_2017 <chr> "Low income", "Upper middle income", "Upper middle income", "High income", "Lower middle income", "Hi...

class_plot<-ggplot(aid_all, aes(y=aid, x=year))
class_plot+geom_point()

class_plot+geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)

class_plot+
  geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)+
  coord_cartesian(ylim = c(0,10000000000))

class_plot+
  geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)+
  ylim(0,10000000000)

class_plot+
  geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)+
  facet_wrap(~gapminders_8_subregions)+
  scale_color_discrete(name="World Bank Income Group 2017")

aid_2010 <- 
  country_aid_clean %>%
  filter(year == 2010)

class_hist<-ggplot(aid_2010, aes(aid))
class_hist+geom_histogram()

class_hist2<-ggplot(aid_2010, aes(log(aid)))
class_hist2+geom_histogram(binwidth=1)

class_hist+geom_histogram(aes(fill=gapminders_4_world_regions))

class_hist+geom_density(aes(color=gapminders_4_world_regions))

class_hist+
  geom_col(aes(y=aid, x=gapminders_4_world_regions, fill=world_bank_income_group_2017),position = "stack")

library(RColorBrewer)

class_hist+geom_boxplot(aes(y=aid, x=gapminders_4_world_regions, fill=gapminders_4_world_regions))+
  ggtitle("Aid 2010", " by Gapminder's 4 World Regions")+
  xlab("Gapminder's 4 World Regions") +
  ylab("Aid") +
  scale_fill_brewer(palette="Dark2")

class_hist+geom_boxplot(aes(y=aid, x=gapminders_4_world_regions, fill=gapminders_4_world_regions))+
  ggtitle("Aid 2010", " by Gapminder's 4 World Regions")+
  xlab("Gapminder's 4 World Regions") +
  ylab("Aid") +
  scale_fill_brewer(palette="Dark2")+
  geom_hline(yintercept=med, linetype=3)

Error in data.frame(yintercept = yintercept) : object 'med' not found

top_variance <- aid_all %>%
  group_by(country) %>%
  mutate(variance = var(aid, na.rm = TRUE)) %>%
  arrange(desc(variance)) %>%
  distinct(country, variance) %>%
  ungroup() %>%
  slice(1:10)
top_variance <- aid_all %>%
  filter(country %in% top_variance$country)
top_variance

plot_ly(aid_subset, x = ~year, y = ~aid, type = "scatter",color = ~factor(country))

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

dmddata_sub<-as.data.frame(subset(dmddata, select=c("carat","cut","color","clarity", "price"))) %>%
  group_by(carat, cut, color, clarity) %>%
  summarise_each(funs(mean))

`summarise_each()` is deprecated.
Use `summarise_all()`, `summarise_at()` or `summarise_if()` instead.
To map `funs` over all variables, use `summarise_all()`

dmddata_sub20+